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This module will provide an introduction to time series models in common use and their use for predicting future observations and/or estimating unobservable components like trend and seasonal effects.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

This module explores the properties of a wide range of models and statistical techniques used to describe and analyse Economic and Financial time series data. The aim is to highlight the usefulness of theoretical time series techniques for understanding data patterns and explore dynamic causal linkages.

Computer science is now a mature discipline, with a well defined curriculum, for example from the ACM/IEEE joint task force, or the UK's Quality Assurance Agency. Any student of computer science should be familiar with a range of topics such as computability, algorithms, computational complexity, computer design, programming language design, programming methodology, data structures, information retrieval, parallel and distributed computing, computer networks, cyber security and artificial intelligence. In addition, computing students should be aware of the effects their field has had, and will continue to have, on individuals, organisations, and society. This module gives an opportunity to review the breadth of computer science, to focus on some key ideas, and to reflect on its wider impact.

Computer science is now a mature discipline, with a well defined curriculum, for example from the ACM/IEEE joint task force, or the UK's Quality Assurance Agency. Any student of computer science should be familiar with a range of topics such as computability, algorithms, computational complexity, computer design, programming language design, programming methodology, data structures, information retrieval, parallel and distributed computing, computer networks, cyber security and artificial intelligence. In addition, computing students should be aware of the effects their field has had, and will continue to have, on individuals, organisations, and society. This module gives an opportunity to review the breadth of computer science, to focus on some key ideas, and to reflect on its wider impact.

Evolution is a changing model of how we understand life. The Darwin-Wallace model, its integration with Mendellian genetics and its formalisation in the Modern Synthesis built a stable backbone for evolutionary biology over the last 150 years. Today, however, the field is expanding scientifically and conceptually in many new ways. The integration of evolutionary thinking with developmental biology has been one of the biggest extentions in the last decades and more recently there is growing awareness that evolutionary and ecological timescales cannot be treated separately as they have been. Meanwhile, new understanding of epigenetic inheritance, niche construction and phenotypic plasticity challenge conventional frameworks and assumptions – motivating interest in an ‘Extended Evolutionary Synthesis’. The EES is a novel way of looking at evolutionary phenomena, which, rather than replacing the Modern Synthesis, seeks to enhance and energise evolutionary thinking. It focuses on four converging themes, Developmental bias, Developmental plasticity, Inclusive inheritance and Niche Construction (see http://extendedevolutionarysynthesis.com/ for more details).

In this module we will introduce the relevant topics from pre-Darwinian ideas right up to current thinking and outstanding research questions. We will also introduce practical skills in evolutionary sciences, which will be further expanded in the BIOL6089 module in semester 2.

Altogether, we will explore the ways that evolutionary biology interfaces with 21st century science.